Marcello Restelli

Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story ICML 2025 Achieving $\widetilde\mathcalO(\sqrtT)$ Regret in Average-Reward POMDPs with Known Observation Models AISTATS 2025 Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement Learning AISTATS 2025 Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning NIPS 2024 Optimal Multi-Fidelity Best-Arm Identification NIPS 2024 Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs COLT 2024 Autoregressive Bandits AISTATS 2024 Parameterized Projected Bellman Operator AAAI 2024 How to Explore with Belief: State Entropy Maximization in POMDPs ICML 2024 Best Arm Identification for Stochastic Rising Bandits ICML 2024 Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning ICLR 2024 Factored-Reward Bandits with Intermediate Observations ICML 2024 Information Capacity Regret Bounds for Bandits with Mediator Feedback JMLR 2024 No-Regret Reinforcement Learning in Smooth MDPs ICML 2024 Graph-Triggered Rising Bandits ICML 2024 A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics NIPS 2024 Online Markov Decision Processes Configuration with Continuous Decision Space AAAI 2024 Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs NIPS 2024 Bandits with Ranking Feedback NIPS 2024 Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control AAAI 2023 Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization AAAI 2023 Simultaneously Updating All Persistence Values in Reinforcement Learning AAAI 2023 Dynamic Pricing with Volume Discounts in Online Settings AAAI 2023 Truncating Trajectories in Monte Carlo Reinforcement Learning ICML 2023 Dynamical Linear Bandits ICML 2023 Towards Theoretical Understanding of Inverse Reinforcement Learning ICML 2023 On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation UAI 2023 Convex Reinforcement Learning in Finite Trials JMLR 2023 Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach NIPS 2023 Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning NIPS 2023 A Tale of Sampling and Estimation in Discounted Reinforcement Learning AISTATS 2023 Tight Performance Guarantees of Imitator Policies with Continuous Actions AAAI 2023 Stochastic Rising Bandits ICML 2022 Challenging Common Assumptions in Convex Reinforcement Learning NIPS 2022 Multi-Fidelity Best-Arm Identification NIPS 2022 Off-Policy Evaluation with Deficient Support Using Side Information NIPS 2022 Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization AAAI 2022 Unsupervised Reinforcement Learning in Multiple Environments AAAI 2022 Reward-Free Policy Space Compression for Reinforcement Learning AISTATS 2022 Finite Sample Analysis of Mean-Volatility Actor-Critic for Risk-Averse Reinforcement Learning AISTATS 2022 Goal-Directed Planning via Hindsight Experience Replay ICLR 2022 Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning ICML 2022 Delayed Reinforcement Learning by Imitation ICML 2022 The Importance of Non-Markovianity in Maximum State Entropy Exploration ICML 2022 Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts IJCAI 2022 Learning in Markov games: Can we exploit a general-sum opponent? UAI 2022 Meta-Reinforcement Learning by Tracking Task Non-stationarity IJCAI 2021 Gaussian Approximation for Bias Reduction in Q-Learning JMLR 2021 Learning in Non-Cooperative Configurable Markov Decision Processes NIPS 2021 Provably Efficient Learning of Transferable Rewards ICML 2021 Leveraging Good Representations in Linear Contextual Bandits ICML 2021 Time-variant variational transfer for value functions UAI 2021 Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection NIPS 2021 Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning NIPS 2021 Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach JMLR 2021 MushroomRL: Simplifying Reinforcement Learning Research JMLR 2021 Newton Optimization on Helmholtz Decomposition for Continuous Games AAAI 2021 Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate AAAI 2021 Policy Optimization as Online Learning with Mediator Feedback AAAI 2021 An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits NIPS 2020 Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning ICML 2020 Sequential Transfer in Reinforcement Learning with a Generative Model ICML 2020 An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies AAAI 2020 Importance Sampling Techniques for Policy Optimization JMLR 2020 Risk-Averse Trust Region Optimization for Reward-Volatility Reduction IJCAI 2020 Sharing Knowledge in Multi-Task Deep Reinforcement Learning ICLR 2020 Gradient-Aware Model-Based Policy Search AAAI 2020 Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration AISTATS 2020 Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions AISTATS 2020 A Novel Confidence-Based Algorithm for Structured Bandits AISTATS 2020 Inverse Reinforcement Learning from a Gradient-based Learner NIPS 2020 Transfer of Samples in Policy Search via Multiple Importance Sampling ICML 2019 Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters NIPS 2019 Reinforcement Learning in Configurable Continuous Environments ICML 2019 Optimistic Policy Optimization via Multiple Importance Sampling ICML 2019 Stochastic Variance-Reduced Policy Gradient ICML 2018 Configurable Markov Decision Processes ICML 2018 Policy Optimization via Importance Sampling NIPS 2018 Transfer of Value Functions via Variational Methods NIPS 2018 Importance Weighted Transfer of Samples in Reinforcement Learning ICML 2018 Adaptive Batch Size for Safe Policy Gradients NIPS 2017 Compatible Reward Inverse Reinforcement Learning NIPS 2017 Boosted Fitted Q-Iteration ICML 2017 Estimating Maximum Expected Value through Gaussian Approximation ICML 2016 Sparse Multi-Task Reinforcement Learning NIPS 2014 Safe Policy Iteration ICML 2013 Adaptive Step-Size for Policy Gradient Methods NIPS 2013 Transfer from Multiple MDPs NIPS 2011 Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods NIPS 2007